IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0179602.html
   My bibliography  Save this article

Immunohistochemiocal subtyping using CK20 and CK5 can identify urothelial carcinomas of the upper urinary tract with a poor prognosis

Author

Listed:
  • Danijel Sikic
  • Bastian Keck
  • Sven Wach
  • Helge Taubert
  • Bernd Wullich
  • Peter J Goebell
  • Andreas Kahlmeyer
  • Peter Olbert
  • Philipp Isfort
  • Wilhelm Nimphius
  • Arndt Hartmann
  • Johannes Giedl
  • on behalf of the Bridge Consortium

Abstract

Purpose: Genome-wide analyses revealed basal and luminal subtypes of urothelial carcinomas of the bladder. It is unknown if this subtyping can also be applied to upper tract urothelial carcinomas. Materials and methods: Tumor samples from 222 patients with upper tract urothelial carcinomas who were treated with radical nephroureterectomy were analyzed for the expression of seven basal/luminal immunohistochemical markers (CK5, EGFR, CD44, CK20, p63, GATA3, FOXA1). Results: Hierarchical clustering revealed a basal-like subtype (enrichment of CK5, EGFR and CD44) in 23.9% and a luminal-like subtype (enrichment of CK20, GATA3, p63 and FOXA1) in 13.1% of the patients. In 60.8%, little to no markers were expressed, whereas markers of both subtypes were expressed in 2.2%. By using CK5 and CK20 as surrogate markers for the basal and luminal subtypes, we defined four subtypes of upper tract urothelial carcinomas: (i) exclusively CK20 positive and CK5 negative (CK20+/CK5-), (ii) exclusively CK5 positive and CK20 negative (CK20-/ CK5+), (iii) both markers positive (CK20+/CK5+) and (iv) both markers negative (CK20-/CK5-). A receiver-operator analysis provided the optimal cut-off values for this discrimination. An immunoreactive score >1 for CK5 and >6 for CK20 were defined as positive. In multivariate Cox’s regression analysis, the CK20+/CK5- subtype was an independent negative prognostic marker with a 3.83-fold increased risk of cancer-specific death (p = 0.02) compared to the other three subtypes. Conclusions: Immunohistochemical subgrouping of upper tract urothelial carcinomas by analyzing CK5 and CK20 expression can be performed in a routine setting and can identify tumors with a significantly worse cancer-specific survival prognosis.

Suggested Citation

  • Danijel Sikic & Bastian Keck & Sven Wach & Helge Taubert & Bernd Wullich & Peter J Goebell & Andreas Kahlmeyer & Peter Olbert & Philipp Isfort & Wilhelm Nimphius & Arndt Hartmann & Johannes Giedl & on, 2017. "Immunohistochemiocal subtyping using CK20 and CK5 can identify urothelial carcinomas of the upper urinary tract with a poor prognosis," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-13, June.
  • Handle: RePEc:plo:pone00:0179602
    DOI: 10.1371/journal.pone.0179602
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179602
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0179602&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0179602?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Charles M. Perou & Therese Sørlie & Michael B. Eisen & Matt van de Rijn & Stefanie S. Jeffrey & Christian A. Rees & Jonathan R. Pollack & Douglas T. Ross & Hilde Johnsen & Lars A. Akslen & Øystein Flu, 2000. "Molecular portraits of human breast tumours," Nature, Nature, vol. 406(6797), pages 747-752, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Xi & Hoadley, Katherine A. & Hannig, Jan & Marron, J.S., 2023. "Jackstraw inference for AJIVE data integration," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    2. Egashira, Kento & Yata, Kazuyoshi & Aoshima, Makoto, 2024. "Asymptotic properties of hierarchical clustering in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    3. María Elena Martínez & Jonathan T Unkart & Li Tao & Candyce H Kroenke & Richard Schwab & Ian Komenaka & Scarlett Lin Gomez, 2017. "Prognostic significance of marital status in breast cancer survival: A population-based study," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    4. Yishai Shimoni, 2018. "Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-15, February.
    5. Anna Dvorkin-Gheva & John A Hassell, 2014. "Identification of a Novel Luminal Molecular Subtype of Breast Cancer," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
    6. Apostolos Zaravinos & George I Lambrou & Ioannis Boulalas & Dimitris Delakas & Demetrios A Spandidos, 2011. "Identification of Common Differentially Expressed Genes in Urinary Bladder Cancer," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-28, April.
    7. Yuru Bai & Lu Qiao & Ning Xie & Yongquan Shi & Na Liu & Jinhai Wang, 2017. "Expression and prognosis analyses of the Tob/BTG antiproliferative (APRO) protein family in human cancers," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-12, September.
    8. Yoo-Ah Kim & Stefan Wuchty & Teresa M Przytycka, 2011. "Identifying Causal Genes and Dysregulated Pathways in Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-13, March.
    9. Wei-Ching Lo & Wen Li & Ella F Jones & David C Newitt & John Kornak & Lisa J Wilmes & Laura J Esserman & Nola M Hylton, 2016. "Effect of Imaging Parameter Thresholds on MRI Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-12, February.
    10. Radhakrishnan Nagarajan & Marco Scutari, 2013. "Impact of Noise on Molecular Network Inference," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    11. R Joseph Bender & Feilim Mac Gabhann, 2013. "Expression of VEGF and Semaphorin Genes Define Subgroups of Triple Negative Breast Cancer," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-15, May.
    12. Deepak Poduval & Zuzana Sichmanova & Anne Hege Straume & Per Eystein Lønning & Stian Knappskog, 2020. "The novel microRNAs hsa-miR-nov7 and hsa-miR-nov3 are over-expressed in locally advanced breast cancer," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-23, April.
    13. Zhiguang Huo & Li Zhu & Tianzhou Ma & Hongcheng Liu & Song Han & Daiqing Liao & Jinying Zhao & George Tseng, 2020. "Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 1-22, April.
    14. Markus Ringnér & Erik Fredlund & Jari Häkkinen & Åke Borg & Johan Staaf, 2011. "GOBO: Gene Expression-Based Outcome for Breast Cancer Online," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    15. Casey S Greene & Olga G Troyanskaya, 2012. "Chapter 2: Data-Driven View of Disease Biology," PLOS Computational Biology, Public Library of Science, vol. 8(12), pages 1-8, December.
    16. Michael X Gleason & Tengiz Mdzinarishvili & Simon Sherman, 2012. "Breast Cancer Incidence in Black and White Women Stratified by Estrogen and Progesterone Receptor Statuses," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-9, November.
    17. Mark Reimers, 2010. "Making Informed Choices about Microarray Data Analysis," PLOS Computational Biology, Public Library of Science, vol. 6(5), pages 1-7, May.
    18. Alan A. Arslan & Yian Zhang & Nedim Durmus & Sultan Pehlivan & Adrienne Addessi & Freya Schnabel & Yongzhao Shao & Joan Reibman, 2021. "Breast Cancer Characteristics in the Population of Survivors Participating in the World Trade Center Environmental Health Center Program 2002–2019," IJERPH, MDPI, vol. 18(14), pages 1-11, July.
    19. Sandra M. Rocha & Sílvia Socorro & Luís A. Passarinha & Cláudio J. Maia, 2022. "Comprehensive Landscape of STEAP Family Members Expression in Human Cancers: Unraveling the Potential Usefulness in Clinical Practice Using Integrated Bioinformatics Analysis," Data, MDPI, vol. 7(5), pages 1-48, May.
    20. Martin H van Vliet & Christiaan N Klijn & Lodewyk F A Wessels & Marcel J T Reinders, 2007. "Module-Based Outcome Prediction Using Breast Cancer Compendia," PLOS ONE, Public Library of Science, vol. 2(10), pages 1-10, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0179602. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.